def get_activator_aids(dev_split, test_split, topological_ftrs=True):
data_name = "Aids"
ext_train = ExternalData(AidsAllExternalDataParams())
ds_params = AidsDatasetAllParams()
if not topological_ftrs:
ds_params.FEATURES = []
ds = BilinearDataset(ds_params, external_data=ext_train)
activator_params = AidsBilinearActivatorParams()
activator_params.TEST_SPLIT = test_split
activator_params.DEV_SPLIT = dev_split
module_params = AidsLayeredBilinearModuleParams(
ftr_len=ds.len_features, embed_vocab_dim=ext_train.len_embed())
return data_name, BilinearActivator(LayeredBilinearModule(module_params),
activator_params, ds)
def get_params_by_dataset(data):
dict_classes = {
"AIDS": [
AidsDatasetAllParams, AidsLayeredBilinearModuleParams,
AidsBilinearActivatorParams,
ExternalData(AidsAllExternalDataParams()), False
],
"PROTEIN": [
ProteinDatasetAllParams, ProteinLayeredBilinearModuleParams,
ProteinBilinearActivatorParams,
ExternalData(ProteinAllExternalDataParams()), True
],
"MUTAGEN": [
MutagenDatasetAllParams, MutagenLayeredBilinearModuleParams,
MutagenBilinearActivatorParams,
ExternalData(MutagenAllExternalDataParams()), False
],
"GREC": [
GrecDatasetAllParams, GrecLayeredBilinearModuleParams,
GrecBilinearActivatorParams,
ExternalData(GrecAllExternalDataParams()), True
]
}
return dict_classes[data]
x0 = torch.cat([x0] + list_embed, dim=2)
x1 = x0
self._sync()
for i in range(self._num_layers):
x1 = self._linear_layers[i](A, x1)
x2 = self._bilinear_layer(A, x0, x1)
return x2
if __name__ == "__main__":
from dataset.datset_sampler import ImbalancedDatasetSampler
from params.aids_params import AidsAllExternalDataParams, AidsDatasetAllParams
from dataset.dataset_external_data import ExternalData
from dataset.dataset_model import BilinearDataset
ext_train = ExternalData(AidsAllExternalDataParams())
ds = BilinearDataset(AidsDatasetAllParams(), external_data=ext_train)
dl = DataLoader(dataset=ds,
collate_fn=ds.collate_fn,
batch_size=64,
sampler=ImbalancedDatasetSampler(ds))
m_params = LayeredBilinearModuleParams(
ftr_len=ds.len_features, embed_vocab_dim=ext_train.len_embed())
m_params.EMBED_DIMS = [20, 20]
module = LayeredBilinearModule(m_params)
# module = BilinearModule(BilinearModuleParams())
for i, (_A, _D, _x0, _l) in enumerate(dl):
_x2 = module(_A, _D, _x0)
e = 0
config + ftrs
res_list.append(config_line)
with open(os.path.join("grid_results", file_name.strip(".txt") + "_analyzed.csv"), "wt") as f:
writer = csv.writer(f)
writer.writerows(res_list)
if __name__ == "__main__":
# n = int(sys.argv[1])
n = 0
if n == 0:
GridSearch(AidsDatasetAllParams, AidsLayeredBilinearModuleParams, AidsBilinearActivatorParams,
ExternalData(AidsAllExternalDataParams()), multi_class=False).go("_Aids")
#
# elif n == 1:
# GridSearch(WebDatasetAllParams, WebLayeredBilinearModuleParams, WebBilinearActivatorParams,
# ExternalData(WebAllExternalDataParams()), multi_class=True,
# layers=[[[None, 50], [50, 25]], [[None, 200], [200, 100], [100, 50]]]).go("_Web")
#
# elif n == 2:
# GridSearch(MutagenDatasetAllParams, MutagenLayeredBilinearModuleParams, MutagenBilinearActivatorParams,
# ExternalData(MutagenAllExternalDataParams()), multi_class=False).go("_Mutagen")
#
# elif n == 3:
# GridSearch(ProteinDatasetAllParams, ProteinLayeredBilinearModuleParams, ProteinBilinearActivatorParams,
# ExternalData(ProteinAllExternalDataParams()), multi_class=True,
# layers=[[[None, 25], [50, 25]], [[None, 100], [100, 50], [50, 25]]]).go("_Protein")
#
with open(
os.path.join("grid_results",
file_name.strip(".txt") + "_analyzed.csv"),
"wt") as f:
writer = csv.writer(f)
writer.writerows(res_list)
if __name__ == "__main__":
n = 8 # int(sys.argv[1])
if n == 0:
GridSearch(AidsDatasetAllParams,
AidsLayeredBilinearModuleParams,
AidsBilinearActivatorParams,
ExternalData(AidsAllExternalDataParams()),
multi_class=False).go("_Aids")
elif n == 1:
GridSearch(WebDatasetAllParams,
WebLayeredBilinearModuleParams,
WebBilinearActivatorParams,
ExternalData(WebAllExternalDataParams()),
multi_class=True).go("_Web")
elif n == 2:
GridSearch(MutagenDatasetAllParams,
MutagenLayeredBilinearModuleParams,
MutagenBilinearActivatorParams,
ExternalData(MutagenAllExternalDataParams()),
multi_class=False).go("_Mutagen")